Rate My Professor Trevelyan McKinley

McKinley's research specializations encompass simulation-based Bayesian inference for epidemic models, efficient model comparison techniques for high-dimensional outputs, and inference methods that do not rely on explicit likelihood functions. His work spans infectious diseases in wildlife, such as bovine tuberculosis transmission in badger populations, human epidemics including COVID-19 meta-population modeling in England and Wales, and precision approaches to type 2 diabetes diagnosis and treatment tolerability prediction. Key publications include 'Inference in Epidemic Models without Likelihoods' (2009), 'Simulation-based Bayesian inference for epidemic models' (2014), 'Emulating computer models with high-dimensional count output' (2025), 'Dirichlet process mixture models to impute missing predictor variables in electronic health records' (2024), and 'Evaluating prediction of short-term tolerability of five type 2 diabetes medications' (2025). With over 140 research contributions and more than 4,400 citations on Google Scholar, his influence is evident in collaborations on Wellcome Trust-funded projects for diabetes precision diagnosis and UKRI grants for Bayesian modeling of wildlife diseases. McKinley supervises PhD studentships on cutting-edge Bayesian inference applied to bovine tuberculosis heterogeneity in wild badgers and contributes to interdisciplinary efforts in health statistics and ecology.